Regional approach to fMRI data analysis using hemodynamic response modeling

Independent component analysis (ICA) has proved to be a powerful method for exploratory analysis of functional magnetic resonance imaging (fMRI) data. It has been used to uncover unexpected activations in fMRI data derived from brain activation. ICA has been used to characterize other sources of variability in the fMRI signal besides task-related activity, as well as challenging some of the assumptions inherent in other fMRI analysis methods. As a data-driven fMRI analysis technique, the philosophy of ICA is often in disagreement with hypothesis-driven methods. By exploiting the fact that much of fMRI data has deterministic spatial-temporal structure, a scheme employing ICA denoising and least squares (LS) estimation of the evoked hemodynamic response (HDR) is proposed. Simulations suggest that the method is more robust to different noise models compared to naive application of LS. The result is a considerably increased level of significance of activation for a given voxel but still qualitatively similar spatial distribution of activations over all voxels. This suggests that the proposed method has the potential to substantially reduce total scanning time requirements to achieve the same level of statistically significant activation.

Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.

SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis

Independent Component Analysis (ICA) is a promising tool for the analysis of functional magnetic resonance imaging (fMRI) time series. In these studies, mostly assumed is a spatially independent component map of fMRI data (spatial ICA). In this paper, we assume that the temporal courses of the signal and noises are independent within a Tiny spatial domain (temporal ICA). Then with fast-ICA algorithm, spatially neighboring fMRI data were blindly separated into several temporal courses and were preassumed to be formed by a signal time course and several noise time courses where the signal has the largest correlation coefficient with the reference signal. The final functional imaging was completed for the signals obtained from each voxel. Simulations showed that compared with the spatial ICA method, the new temporal ICA method is more effective than the spatial ICA in detecting weak signal in a fMRI dataset. As background noise, the simulations include simulated Gaussian noise and fMRI data without stimulation. Finally, vivo fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex and that evoked by auditory stimuli are mainly in the region of the primary temporal cortex.

SummaryHumans show a remarkable ability to discriminate others' gaze direction, even though a given direction can be conveyed by many physically dissimilar configurations of different eye positions and head views. For example, eye contact can be signaled by a rightward glance in a left-turned head or by direct gaze in a front-facing head. Such acute gaze discrimination implies considerable perceptual invariance. Previous human research found that superior temporal sulcus (STS) responds preferentially to gaze shifts [1], but the underlying representation that supports such general responsiveness remains poorly understood. Using multivariate pattern analysis (MVPA) of human functional magnetic resonance imaging (fMRI) data, we tested whether STS contains a higher-order, head view-invariant code for gaze direction. The results revealed a finely graded gaze direction code in right anterior STS that was invariant to head view and physical image features. Further analyses revealed similar gaze effects in left anterior STS and precuneus. Our results suggest that anterior STS codes the direction of another's attention regardless of how this information is conveyed and demonstrate how high-level face areas carry out fine-grained, perceptually relevant discrimination through invariance to other face features.

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

Independent Component Analysis (ICA) is a popular method that uses statistical principles to separate the mixture into statistically independent non-Gaussian sources. It has been well used in functional Magnetic Resonance Imaging (fMRI) data. However, real fMRI data can rarely be accurately modeled as mixtures of independent components, the convergence of ICA may be impaired. This paper is based on the idea of preconditioned ICA for real data (Picard), which involves a preprocessing L-BFGS strategy based on orthogonal matrix sets. In this study, we designed an experiment to validate the idea that Picard can improve ICA algorithms such as Infomax, Extended-Infomax, and FastICA, respectively named Picard 1, Picard 2, and Picard 3, for fMRI data analysis. Three Picard versions were performed on the simulated and noisy fMRI mixtures to verify the ability to separate independent sources. Experimental results showed that Picard 3 outperformed Picard 1 and Picard 2 on both noiseless and noisy simulated fMRI data, which implied the priority of Picard 3 in fMRI data analysis.

Optimizing ICA in fMRI using information on spatial regularities of the sources

In recent years, the discovery of brain effective connectivity (EC) networks through computational analysis of functional magnetic resonance imaging (fMRI) data has gained prominence in neuroscience and neuroimaging. However, owing to the influence of diverse factors during data collection and processing, fMRI data typically exhibits high noise and limited sample characteristics, consequently leading to suboptimal performance of current methods. In this paper, we propose a novel brain effective connectivity discovery method based on meta-reinforcement learning, called MetaRLEC. The method mainly consists of three modules: actor, critic, and meta-critic. MetaRLEC first employs an encoder-decoder framework: the encoder utilizing a Transformer, converts noisy fMRI data into a state embedding; the decoder employing bidirectional LSTM, discovers brain region dependencies from the state and generates actions (EC networks). Then a critic network evaluates these actions, incentivizing the actor to learn higher-reward actions amidst the high-noise setting. Finally, a meta-critic framework facilitates online learning of historical state-action pairs, integrating an action-value neural network and supplementary training losses to enhance the model's adaptability to small-sample fMRI data. We conduct comprehensive experiments on both simulated and real-world data to demonstrate the efficacy of our proposed method.

Whether signals from different sensory modalities converge and interact within primary cortices in humans is unresolved, despite emerging evidence in animals. This is partially because of debates concerning the appropriate analyses of functional magnetic resonance imaging (fMRI) data in response to multisensory phenomena. Using event-related fMRI, we observed that simple auditory stimuli (noise bursts) activated primary visual cortices and that simple visual stimuli (checkerboards) activated primary auditory cortices, indicative of multisensory convergence. Moreover, analyses of blood oxygen level-dependent response dynamics revealed facilitation of hemodynamic response peak latencies and slopes for multisensory auditory-visual stimuli versus either unisensory condition, indicative of multisensory interactions within primary sensory cortices. Neural processing at the lowest cortical levels can be modulated by interactions between the senses. Temporal information in fMRI data can reveal these modulations and overcome analytic and interpretational challenges of more traditional procedures. In addition to providing an essential translational link with animal models, these results suggest that longstanding notions of cortical organization need to be revised to include multisensory interactions as an inherent component of functional brain organization.

A new adaptive signal-preserving technique for noise suppression in functional magnetic resonance imaging (fMRI) data is proposed based on spectrum subtraction. The proposed technique estimates a model for the power spectrum of random noise from the acquired data. This model is used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data for event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components. Moreover, further analysis using principal component analysis (PCA) and independent component analysis (ICA) shows a significant improvement in both convergence and clarity of results when the new technique is used. This suggests the value of the new technique as a useful preprocessing step for this type of signal.

A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process

A two-step super-Gaussian independent component analysis approach for fMRI data